Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory63.3 B

Variable types

Categorical3
Numeric3
Text1

Alerts

mbr_no has constant value ""Constant
tms is highly overall correlated with race_dayHigh correlation
race_day is highly overall correlated with tmsHigh correlation
stnd_year is highly imbalanced (80.6%)Imbalance
start_photo_url has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:01:51.737304
Analysis finished2023-12-10 10:01:54.976059
Duration3.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

stnd_year
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019
97 
2021
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2021
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 97
97.0%
2021 3
 
3.0%

Length

2023-12-10T19:01:55.160076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:55.377991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 97
97.0%
2021 3
 
3.0%

tms
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.62
Minimum10
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:01:55.633993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q123.75
median34
Q341
95-th percentile47.05
Maximum51
Range41
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation11.210367
Coefficient of variation (CV)0.35453405
Kurtosis-0.90519734
Mean31.62
Median Absolute Deviation (MAD)8
Skewness-0.38416175
Sum3162
Variance125.67232
MonotonicityNot monotonic
2023-12-10T19:01:56.053813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
42 7
 
7.0%
30 7
 
7.0%
40 6
 
6.0%
12 6
 
6.0%
25 5
 
5.0%
41 5
 
5.0%
39 5
 
5.0%
36 4
 
4.0%
34 4
 
4.0%
16 3
 
3.0%
Other values (28) 48
48.0%
ValueCountFrequency (%)
10 1
 
1.0%
11 2
 
2.0%
12 6
6.0%
13 2
 
2.0%
14 1
 
1.0%
15 1
 
1.0%
16 3
3.0%
18 2
 
2.0%
20 1
 
1.0%
21 3
3.0%
ValueCountFrequency (%)
51 2
 
2.0%
49 2
 
2.0%
48 1
 
1.0%
47 2
 
2.0%
46 1
 
1.0%
45 2
 
2.0%
44 1
 
1.0%
43 3
3.0%
42 7
7.0%
41 5
5.0%

mbr_no
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T19:01:56.319596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:56.560583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

day_ord
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3
42 
2
33 
1
25 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row3
5th row1

Common Values

ValueCountFrequency (%)
3 42
42.0%
2 33
33.0%
1 25
25.0%

Length

2023-12-10T19:01:56.798134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:01:57.132626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 42
42.0%
2 33
33.0%
1 25
25.0%

race_no
Real number (ℝ)

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.09
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:01:57.309153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median8
Q312
95-th percentile15.05
Maximum18
Range17
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation4.7802096
Coefficient of variation (CV)0.59087882
Kurtosis-1.1634106
Mean8.09
Median Absolute Deviation (MAD)4
Skewness0.11588956
Sum809
Variance22.850404
MonotonicityNot monotonic
2023-12-10T19:01:57.525936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
12 10
 
10.0%
3 9
 
9.0%
2 8
 
8.0%
13 8
 
8.0%
1 8
 
8.0%
10 7
 
7.0%
8 7
 
7.0%
6 6
 
6.0%
15 6
 
6.0%
9 6
 
6.0%
Other values (8) 25
25.0%
ValueCountFrequency (%)
1 8
8.0%
2 8
8.0%
3 9
9.0%
4 5
5.0%
5 5
5.0%
6 6
6.0%
7 5
5.0%
8 7
7.0%
9 6
6.0%
10 7
7.0%
ValueCountFrequency (%)
18 1
 
1.0%
17 2
 
2.0%
16 2
 
2.0%
15 6
6.0%
14 3
 
3.0%
13 8
8.0%
12 10
10.0%
11 2
 
2.0%
10 7
7.0%
9 6
6.0%

race_day
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean794.23
Minimum310
Maximum1229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:01:57.797454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum310
5-th percentile322.95
Q1614.25
median823
Q31018
95-th percentile1201.35
Maximum1229
Range919
Interquartile range (IQR)403.75

Descriptive statistics

Standard deviation268.53891
Coefficient of variation (CV)0.33811228
Kurtosis-0.9696588
Mean794.23
Median Absolute Deviation (MAD)201
Skewness-0.34922106
Sum79423
Variance72113.149
MonotonicityNot monotonic
2023-12-10T19:01:58.068191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
421 3
 
3.0%
728 3
 
3.0%
1027 3
 
3.0%
1025 3
 
3.0%
1018 3
 
3.0%
1012 3
 
3.0%
323 3
 
3.0%
1102 2
 
2.0%
1013 2
 
2.0%
901 2
 
2.0%
Other values (58) 73
73.0%
ValueCountFrequency (%)
310 1
 
1.0%
315 1
 
1.0%
316 1
 
1.0%
322 2
2.0%
323 3
3.0%
324 1
 
1.0%
330 1
 
1.0%
331 1
 
1.0%
406 1
 
1.0%
414 1
 
1.0%
ValueCountFrequency (%)
1229 2
2.0%
1215 2
2.0%
1208 1
1.0%
1201 1
1.0%
1129 1
1.0%
1124 1
1.0%
1116 2
2.0%
1108 1
1.0%
1102 2
2.0%
1101 1
1.0%

start_photo_url
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:01:58.630398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length79
Median length79
Mean length79
Min length79

Characters and Unicode

Total characters7900
Distinct characters35
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowhttp://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1576382470484.jpg
2nd rowhttp://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1632638346705.jpg
3rd rowhttp://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1553937470685.jpg
4th rowhttp://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1553415847183.jpg
5th rowhttp://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1553235106031.jpg
ValueCountFrequency (%)
http://www.kcycle.or.kr/contents/popup/judgephotopopup.do?img=1576382470484.jpg 1
 
1.0%
http://www.kcycle.or.kr/contents/popup/judgephotopopup.do?img=1575020684761.jpg 1
 
1.0%
http://www.kcycle.or.kr/contents/popup/judgephotopopup.do?img=1553331155197.jpg 1
 
1.0%
http://www.kcycle.or.kr/contents/popup/judgephotopopup.do?img=1554017562893.jpg 1
 
1.0%
http://www.kcycle.or.kr/contents/popup/judgephotopopup.do?img=1554538148868.jpg 1
 
1.0%
http://www.kcycle.or.kr/contents/popup/judgephotopopup.do?img=1555224113151.jpg 1
 
1.0%
http://www.kcycle.or.kr/contents/popup/judgephotopopup.do?img=1555828727793.jpg 1
 
1.0%
http://www.kcycle.or.kr/contents/popup/judgephotopopup.do?img=1555822825839.jpg 1
 
1.0%
http://www.kcycle.or.kr/contents/popup/judgephotopopup.do?img=1556960357580.jpg 1
 
1.0%
http://www.kcycle.or.kr/contents/popup/judgephotopopup.do?img=1557032593919.jpg 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:01:59.521399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 700
 
8.9%
o 700
 
8.9%
/ 500
 
6.3%
. 500
 
6.3%
t 500
 
6.3%
u 300
 
3.8%
w 300
 
3.8%
c 300
 
3.8%
g 300
 
3.8%
e 300
 
3.8%
Other values (25) 3500
44.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5100
64.6%
Decimal Number 1300
 
16.5%
Other Punctuation 1200
 
15.2%
Uppercase Letter 200
 
2.5%
Math Symbol 100
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 700
13.7%
o 700
13.7%
t 500
 
9.8%
u 300
 
5.9%
w 300
 
5.9%
c 300
 
5.9%
g 300
 
5.9%
e 300
 
5.9%
h 200
 
3.9%
d 200
 
3.9%
Other values (9) 1300
25.5%
Decimal Number
ValueCountFrequency (%)
5 215
16.5%
1 190
14.6%
6 137
10.5%
7 136
10.5%
3 116
8.9%
2 109
8.4%
0 104
8.0%
8 103
7.9%
4 96
7.4%
9 94
7.2%
Other Punctuation
ValueCountFrequency (%)
/ 500
41.7%
. 500
41.7%
: 100
 
8.3%
? 100
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
P 200
100.0%
Math Symbol
ValueCountFrequency (%)
= 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5300
67.1%
Common 2600
32.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 700
13.2%
o 700
13.2%
t 500
 
9.4%
u 300
 
5.7%
w 300
 
5.7%
c 300
 
5.7%
g 300
 
5.7%
e 300
 
5.7%
h 200
 
3.8%
P 200
 
3.8%
Other values (10) 1500
28.3%
Common
ValueCountFrequency (%)
/ 500
19.2%
. 500
19.2%
5 215
8.3%
1 190
 
7.3%
6 137
 
5.3%
7 136
 
5.2%
3 116
 
4.5%
2 109
 
4.2%
0 104
 
4.0%
8 103
 
4.0%
Other values (5) 490
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
p 700
 
8.9%
o 700
 
8.9%
/ 500
 
6.3%
. 500
 
6.3%
t 500
 
6.3%
u 300
 
3.8%
w 300
 
3.8%
c 300
 
3.8%
g 300
 
3.8%
e 300
 
3.8%
Other values (25) 3500
44.3%

Interactions

2023-12-10T19:01:53.957633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:52.834375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:53.377964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:54.107221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:52.990563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:53.544452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:54.289611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:53.140250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:01:53.784686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:01:59.731983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
stnd_yeartmsday_ordrace_norace_daystart_photo_url
stnd_year1.0000.0000.0000.0000.0001.000
tms0.0001.0000.2050.0000.9981.000
day_ord0.0000.2051.0000.0000.2981.000
race_no0.0000.0000.0001.0000.0001.000
race_day0.0000.9980.2980.0001.0001.000
start_photo_url1.0001.0001.0001.0001.0001.000
2023-12-10T19:01:59.915546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
day_ordstnd_year
day_ord1.0000.000
stnd_year0.0001.000
2023-12-10T19:02:00.051230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
tmsrace_norace_daystnd_yearday_ord
tms1.000-0.1340.9990.0000.116
race_no-0.1341.000-0.1300.0000.000
race_day0.999-0.1301.0000.0000.161
stnd_year0.0000.0000.0001.0000.000
day_ord0.1160.0000.1610.0001.000

Missing values

2023-12-10T19:01:54.555618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:01:54.864321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

stnd_yeartmsmbr_noday_ordrace_norace_daystart_photo_url
02019491321215http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1576382470484.jpg
1202139131926http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1632638346705.jpg
22019131213330http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1553937470685.jpg
32019121311324http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1553415847183.jpg
4201912115322http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1553235106031.jpg
5201911122316http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1552712618502.jpg
6201936126907http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1567840815455.jpg
7202128116709http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1625819854387.jpg
8201937133922http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1569128707150.jpg
920193912101005http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1570262740120.jpg
stnd_yeartmsmbr_noday_ordrace_norace_daystart_photo_url
902019351212831http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1567245093448.jpg
91201934128824http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1566634165737.jpg
92201933136818http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1566108378521.jpg
93201932121810http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1565414357431.jpg
94201931132804http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1564893239804.jpg
952019311213803http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1564827372512.jpg
962019301311728http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1564301256581.jpg
97201930123727http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1564207420410.jpg
98201930114726http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1564122568365.jpg
99201930118726http://www.kcycle.or.kr/contents/popup/judgePhotoPopup.do?img=1564128529000.jpg